In general, much of the work on the generalization gap in deep learning has focused on balanced classification problems. It is well-know, however, that generalization is a major issue in the context of imbalanced classification. In the case of majority classes, a neural network may have a sufficient number and variety of training examples such that it can generalize well to the test set. On the other hand, given i.i.d. sampling of the underlying distribution, the sparsely sampled minority classes are likely to have a wide gap between the training and testing distributions. The generalization gap problem is further compounded in imbalanced learning because the majority classes may contain sub-concepts that overlap with minority classes [1]}, [2]}, [3]}, [4]}. Feldman has shown that minority classes, which share features with majority class sub-concepts, can be treated as "noisy" majority class examples that get misclassified [5]}.
